




"""simple docstring"""




import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  List[str]								=       checkpoints.load_tax_checkpoint(__UpperCamelCase	)
			__lowercase			:  Any								=       flatten_dict(__UpperCamelCase	)
			return flax_params







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  int								=       {}

			__lowercase			:  Optional[int]								=       {
			    '''token_embedder''': '''embeddings''',
			    '''encoder_norm''': '''layernorm''',
			    '''kernel''': '''weight''',
			    '''.out''': '''.output''',
			    '''scale''': '''weight''',
			    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
			    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
			}

			__lowercase			:  List[str]								=       {
			    '''query''': '''attention.query''',
			    '''key''': '''attention.key''',
			    '''value''': '''attention.value''',
			    '''output.dense''': '''output''',
			    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
			    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
			    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
			    '''mlp.''': '''mlp.DenseReluDense.''',
			    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
			    '''self_attention.o''': '''self_attention.attention.o''',
			    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
			    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
			    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									__lowercase			:  Dict								=       '''.'''.join(key[1:]	)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												__lowercase			:  List[Any]								=       new_key.replace(__UpperCamelCase	,				__UpperCamelCase	)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															__lowercase			:  str								=       new_key.replace(__UpperCamelCase	,				__UpperCamelCase	)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												__lowercase			:  int								=       re.sub(R'''layers_(\d+)'''	,				R'''layer.\1'''	,				__UpperCamelCase	)
												__lowercase			:  List[Any]								=       new_key.replace('''encoder'''	,				'''encoder.encoder'''	)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												__lowercase			:  int								=       re.sub(R'''layers_(\d+)'''	,				R'''layer.\1'''	,				__UpperCamelCase	)

									__lowercase			:  Optional[int]								=       flax_dict[key]

			__lowercase			:  Dict								=       {}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									__lowercase			:  Optional[int]								=       torch.from_numpy(converted_dict[key].T	)
						else:
									__lowercase			:  Dict								=       torch.from_numpy(converted_dict[key]	)

			return converted_torch_dict







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False	,				__UpperCamelCase=False	):
			__lowercase			:  Optional[int]								=       get_flax_param(__UpperCamelCase	)

			if not use_large:
						__lowercase			:  int								=       PixaStructVisionConfig()
						__lowercase			:  str								=       PixaStructTextConfig()
			else:
						__lowercase			:  str								=       PixaStructVisionConfig(
						    hidden_size=15_36	,				d_ff=39_68	,				num_attention_heads=24	,				num_hidden_layers=18	)
						__lowercase			:  Dict								=       PixaStructTextConfig(hidden_size=15_36	,				d_ff=39_68	,				num_heads=24	,				num_layers=18	)
			__lowercase			:  List[str]								=       PixaStructConfig(
			    vision_config=encoder_config.to_dict()	,				text_config=decoder_config.to_dict()	,				is_vqa=__UpperCamelCase	)

			__lowercase			:  Dict								=       PixaStructForConditionalGeneration(__UpperCamelCase	)

			__lowercase			:  List[Any]								=       rename_and_convert_flax_params(__UpperCamelCase	)
			model.load_state_dict(__UpperCamelCase	)

			__lowercase			:  Union[str, Any]								=       AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''	)
			__lowercase			:  int								=       PixaStructImageProcessor()
			__lowercase			:  str								=       PixaStructProcessor(image_processor=__UpperCamelCase	,				tokenizer=__UpperCamelCase	)

			if use_large:
						__lowercase			:  List[Any]								=       40_96

			__lowercase			:  Tuple								=       True

			# mkdir if needed
			os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase	)

			model.save_pretrained(__UpperCamelCase	)
			processor.save_pretrained(__UpperCamelCase	)

			print('''Model saved in {}'''.format(__UpperCamelCase	)	)


if __name__ == "__main__":
			a_        =       argparse.ArgumentParser()
			parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
			parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
			parser.add_argument('--use_large', action='store_true', help='Use large model.')
			parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
			a_        =       parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)











"""simple docstring"""


from math import sqrt


def        UpperCAmelCase      (					a__ = 1_00_00_00 ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int

						while num_cuboids <= limit:
												max_cuboid_size += 1
												for sum_shortest_sides in range(2     ,      2 * max_cuboid_size + 1 ):
																		if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
																								num_cuboids += (
																								    min(a__     ,      sum_shortest_sides // 2 )
																								    - max(1     ,      sum_shortest_sides - max_cuboid_size )
																								    + 1
																								)

						return max_cuboid_size


if __name__ == "__main__":
		print(F"""{solution() = }""")